Model training, data pipeline engineering, algorithm research, and performance optimization qualify for substantial R&D tax credits. Average AI/ML startup savings: $300K-$1M+ annually.
AI and machine learning development is the quintessential example of qualifying R&D. Every aspect involves technical uncertainty, experimentation, and iterative improvement - the exact activities Congress designed R&D credits to incentivize.
Whether you're training deep learning models, building recommendation engines, developing computer vision systems, or creating NLP applications, nearly all your ML engineering work qualifies. Model experimentation, hyperparameter tuning, architecture search, and performance optimization are textbook R&D activities.
The typical AI/ML company sees 85-95% of their engineering time qualify - far higher than most industries. With average salaries of $180K+ for ML engineers and data scientists, the credits add up quickly to $300K-$1M+ annually.
Model Development & Training
Neural networks, transformers, ensemble methods
Data Pipeline Engineering
ETL, feature engineering, data quality
Algorithm Research
Novel architectures, optimization techniques
Performance Optimization
Inference speed, model compression, quantization
Nearly all AI/ML development involves experimentation and technical uncertainty. Here's what typically qualifies for R&D credits:
95-100%
Model development is pure R&D - constant experimentation to find what works.
Typical Credits:
2 ML engineers/year = $120K-$180K
80-90%
Data engineering for ML involves significant experimentation and optimization.
Typical Credits:
2 data engineers/year = $100K-$140K
85-95%
Production ML optimization involves constant experimentation for performance gains.
Typical Credits:
2 MLOps engineers/year = $110K-$160K
All of these qualify at 90-100% because they involve solving novel problems with uncertain outcomes through experimentation.
Constant experimentation with architectures, hyperparameters
ETL, feature engineering, data quality automation
Model compression, quantization, TensorRT optimization
Federal Credits:
State Credits (CA example):
Total Annual R&D Tax Benefit:
Federal + State credits combined
$1,159,113
Enough to hire 5+ ML engineers
Many AI/ML companies spend hundreds of thousands on GPU training but don't realize these costs qualify as supplies used in R&D.
Solution:
Cloud compute costs (AWS, GCP, Azure) used for model training, experimentation, and development qualify. Tag resources by purpose and claim dev/training costs.
Companies think only model training qualifies, missing that data pipeline engineering, ETL, and feature engineering involve significant R&D.
Solution:
Data engineering for ML is highly experimental. Building efficient pipelines, cleaning data, and engineering features all qualify at 80-90%.
Data labeling and annotation contractors often aren't included in R&D calculations, leaving significant money on the table.
Solution:
Contractor costs for labeling, annotation, and data work qualify under the 65% rule. Include all 1099 contractors in your calculation.
Companies fine-tuning BERT, GPT, or using pre-trained models think they don't qualify because they didn't build from scratch.
Solution:
Transfer learning, fine-tuning, and model adaptation are all qualifying R&D. The experimentation to adapt models to your use case counts.
A Series A NLP platform for enterprise document understanding. 15 ML engineers/data scientists building custom language models for contract analysis, financial document processing, and semantic search.
Federal:
$531,000
State (CA):
$192,000
— CTO & Co-Founder
Get a free assessment to see how much your AI/ML development qualifies for. Most companies claim $300K-$1M+ annually.
Contingency pricing • No upfront cost • 99% of AI/ML companies qualify